1. Stewart, B. World Cancer Report 2014 / B. Stewart, C. P. Wild. - Geneva : WHO Press, 2015. - 512 p.
2. Программный пакет CellDataMiner для анализа люминесцентных изображений раковых клеток / Е. В. Лисица [и др.] // Информатика. - 2015. - № 4(48). - С. 73-84.
3. Spatial quantitative analysis of fluorescently labeled nuclear structures: problems, methods, pitfalls / O. Ronneberger [et al.] // Chromosome Research. - 2008. - No. 3. - P. 523-562.
4. Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection / J. C. Ang [et al.] // IEEE/ACM Transactions on Computational Biology and Bioinformatics. - 2016. - No. 5. - P. 971-989.
5. Classifying ten types of major cancers based on reverse phase protein array profiles / P. W. Zhang [et al.] // PLoS One. - 2015. - No. 5. - P. 3-7.
6. Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer / J. Sonntag [et al.] // Translational Proteomics. - 2014. - No. 2. - P. 52-59.
7. Kaddi, C. Models for predicting stage in head and neck squamous cell carcinoma using proteomic and transcriptomic data / C. Kaddi, M. D.Wang // IEEE J. of Biomedical and Health Informatics. - 2017. - No. 1. - P. 246-253.
8. Immunosignature system for diagnosis of cancer / P. Stafford [et al.] // Proc. of the National Academy of Sciences of the United States of America. - 2014. - No. 30. - P. 3072-3080.
9. Nguyen, T. Modified AHP for gene selection and cancer classification using type-2 fuzzy logic / T. Nguyen, S. Nahavandi // IEEE Transactions on Fuzzy Systems. - 2016. - No. 2. - P. 273-287.
10. Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification / T. Nguyen [et al.] // PloS One. - 2015. - No. 3.
11. 3-phosphoadenosine 5-phosphosulfate (paps) synthases, naturally fragile enzymes specifically stabilized by nucleotide binding / J. Van den Boom [et al.] // J. of Biological Chemistry. - 2012. - No. 21. - P. 17645-17655.
12. Insights into the classification of small GTPases / D. Heider [et al.] // Advances and Applications in Bioinformatics and Chemistry. - 2010. - No. 3. - P. 15-24.
13. Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? / W. G. Touw [et al.] // Briefings in Bioinformatics. - 2013. - No. 3. - P. 315-326.
14. Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers / J. N. Dybowski [et al.] // BioData Mining. - 2011. - No. 4. - Р. 26-39.
15. Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification / M. Riemenschneider [et al.] // BioData Mining. - 2016. - No. 9. - Р. 10-16.
16. Expression profiling reveals novel pathways in the transformation of melanocytes to melanomas / H. Hoek [et al.] // Cancer Research. - 2004. - No. 15. - P. 5270-5282.
17. Quantitative analysis of estrogen receptor heterogeneity in breast cancer / G. G. Chung [et al.] // Laboratory Investigation. - 2007. - No. 7. - P. 662-669.
18. Camp, R. L. Automated subcellular localization and quantification of protein expression in tissue microarrays / R. L. Camp, G. G. Chung, D. L. Rimm // Nature Medicine. - 2002. - No. 11. - P. 1323-1327.
19. Quantifying estrogen and progesterone receptor expression in breast cancer by digital imaging / M. K. Szesze [et al.] // J. of Histochemistry and Cytochemistry. - 2005. - No. 6. - P. 753-762.
20. Имитационная модель трехканальных люминесцентных изображений популяций раковых клеток / Е. В. Лисица [и др.] // Журнал прикладной спектроскопии. - 2014. - № 6. - С. 907-913.
21. Burger, W. Principles of Digital Image Processing: Core Algorithms / W. Burger, M. Burge. - London : Springer-Verlag, 2009. - 332 p.
22. Jähne, B. Digital Image Processing. Iss. 5 / B. Jähne. - Berlin, Heidelberg : Springer, 2002. - 585 p.
23. Reiss, Th. H. Recognizing Planar Objects using Invariant Image Features / Th. H. Reiss. - Berlin : Springer, 1993. - 186 p.
24. Hu, M. K. Visual pattern recognition by moment invariants / M. K. Hu // IEEE Transactions on Information Theory. - 1962. - No. 8. - P. 179-187.
25. Neumann, U. EFS: an ensemble feature selection tool implemented as R-package and web-application / U. Neumann // BioData Mining. - 2017. - No. 10. - Р. 21-30.
26. Bauer, D. F. Constructing confidence sets using rank statistics / D. F. Bauer // J. of the American Statistical Association. - 1972. - No. 67. - P. 687-690.
27. Yu, L. Efficient feature selection via analysis of relevance and redundancy / L. Yu // J. of Machine Learning Research. - 2004. - No. 5. - P. 1205-1224.
28. Suzuki, N. Developing landscape habitat models for rare amphibians with small geographic ranges: a case study of Siskiyou Mountains salamanders in the western USA / N. Suzuki, D. H. Olson, E. C. Reilly // J. of Machine Learning Research. - 2008. - No. 17. - P. 2197-2218.
29. Novel methods improve prediction of species distributions from occurrence data / J. Elith [et al.] // J. of Space and Time in Ecology. - 2006. - No. 29. - P. 129-151.
30. Yu, L. Efficient feature selection via analysis of relevance and redundancy / L. Yu, H. Liu // J. of Machine Learning Research. - 2004. - No. 5. - P. 1205-1224.
31. Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach / U. Neumann [et al.] // BioData Mining. - 2016. - No. 9. - P. 36-50.
32. Breiman, L. Random forests / L. Breiman // Machine Learning. - 2001. - No. 5. - P. 5-32.
33. Feature selection based on quality of information / J. Liu [et al.] // Neurocomputing. - 2017. - No. 225. - P. 11-22.
34. Measures of rule quality for feature selection in text categorization / E. Montañés [et al.] // Advances in Intelligent Data Analysis V. - 2003. - No. 225. - P. 589-598.
35. Scikit-learn: Machine learning in Python / F. Pedregosa [et al.] // J. of Machine Learning Research. - 2011. - No. 12. - P. 2825-2830.